Privacy-related information can be extracted from the image itself or from its metadata (e.g. user-generated tags).
Zerr et al. [1]} use SIFT features [2]} in conjunction with metadata, including the title and user-generated tags.
Dynamic Multi-Modal Fusion for Privacy Prediction (DMFP) [3]} fuses features from three different modalities (object, scene and tag).
A competence estimation of each modality is performed to determine the best modality whose decisions are fused in the last stage to produce a prediction.
DMFP builds upon the Combination (Comb.) model [4]} that combines scene-based tags extracted from images with convolutional networks with features of the objects detected in the image.
The Gated Fusion model [5]} fuses predictions generated by single-modal models (for objects, scene and tags) and dynamically learns the fusion weights for each modality.
